Analyzing transactional data

ABSTRACT

A system and method for measuring or at least detecting the effect of at least implicit communication on transactions within a social group, optionally such as consumer purchases for example. The group may optionally comprise a social network, a pair, 3 or more individuals and so forth. By “implicit communication” it is meant communication for which there is no record, for example in a database. The aftereffects of such communication may optionally be determined, detected and/or measured through detection and/or measurement of influence, as described in greater detail below. For example, if a first consumer performs purchase of an item and a second consumer later purchases the same or similar item within a given time period, the two purchases may optionally be linked through influence which may in turn have optionally occurred through implicit communication. Optionally, the system and method may also measure or at least detect the effect of explicit communication, for which there is a record, for example in a database that describes the communication itself.

FIELD OF THE INVENTION

The present invention is of a system and method for analyzingtransactional data, and in particular, for such a system and method inwhich implicit communication may be determined and included in theanalysis.

BACKGROUND OF THE INVENTION

Successful marketing of products (including goods and/or services)relies upon many factors; however, a predominant factor is word ofmouth, or the effect of interpersonal communication. Word of mouthmarketing relies upon the recommendation or opinion of a trustedcolleague, friends, or relatives of the consumer. Word of mouthmarketing is effective yet is difficult for merchants and brand ownersto successfully perform.

Various attempts have been made to determine consumer social networks,and hence to promote or influence word of mouth marketing throughparticularly influential members of such networks. However, theseattempts have generally not been successful, despite (or rather becauseof) the massive amount of available data. Thus, merchants frequentlyattempt instead to analyze and predict the behavior of each consumerindividually, and hence disregard such connections.

There are many efforts organizations employ to use the power of word ofmouth.

Undercover marketing—commercially motivate people to recommend a certainproduct (personally or through internet chat rooms, talkbacks etc.) oruse the product or service in public.

Creating buzz—Certain companies use word of mouth advertising agenciesto identify opinion leaders and use them to spread word of mouth. Theseopinion leaders are recruited one by one through web site or personalinterviews.

Targeting trend setters—Certain companies send a product to severalA-list trend setters to spread the word. For example, the book “the daVinci code” was sent to 10,000 industry trend setters for them to createand amplify the word of mouth around the book.

Thus, none of the above attempted solutions successfully uses socialnetworks to determine how to target word of mouth marketing toinfluential members of such networks.

SUMMARY OF THE INVENTION

The background art does not teach or suggest a system or method forsuccessfully determining a social network from transactional or otherdata. The background art also does not teach or suggest such a system ormethod which incorporates implicit communication.

The present invention overcomes these drawbacks of the background art byproviding a system and method for measuring or at least detecting theeffect of at least implicit communication on transactions within asocial group, optionally such as consumer purchases for example. Thegroup may optionally comprise a social network, a pair, 3 or moreindividuals and so forth. By “implicit communication” it is meantcommunication for which there is no record, for example in a database.However, implicit communication is preferably detected according to anaction and/or an effect of an action, such as a transaction for example,which more preferably features some type of recorded data. Theaftereffects of such communication may optionally be determined,detected and/or measured through detection and/or measurement ofinfluence, as described in greater detail below. For example, if a firstconsumer purchases an item and a second consumer later purchases thesame or similar item within a given time period, the two purchases mayoptionally be linked through influence which may in turn have optionallyoccurred through implicit communication.

Optionally, the system and method may also measure or at least detectthe effect of explicit communication, for which there is a record, forexample in a database that describes the communication itself.Non-limiting examples include a telephone call record or an emailrecord. However, it is difficult to determine influence through explicitcommunication alone and in fact the present invention does not relate todetermining influence through such explicit communication alone.

Alternatively or additionally, according to some embodiments, a keymember of a social group is preferably identified and therelationship(s) between the key member and other member(s) of the socialgroup are determined, more preferably including influence(s) by the keymember on other member(s) of the social group and also optionallyincluding influence(s) by other member(s) of the social group on the keymember. Optionally the complete social network is not determined forthis embodiment.

Influence may optionally be active or passive. Active influence occurswhen an individual speaks about a product directly, for example torecommend it, preferably without any commercial incentive. Passiveinfluence occurs through indirect transfer of information betweenindividuals, for example by viewing a product purchased by anindividual. Preferably both types are determined and/or measuredaccording to the present invention.

The passive influence is preferably measured or quantified, therebyresulting in the quantification of a passive transmission coefficient.More preferably, active influence is also measured or quantified,thereby resulting in the quantification of an active transmissioncoefficient.

Preferably, the behavior of the social group is analyzed to form asocial network. Such analysis preferably includes determining arelationship between two or more consumers; and preferably analyzingpassive and/or active influence(s) between them. Most preferably, atleast implicit communication is measured although optionally and mostpreferably, direct communication is measured.

According to some embodiments, person to person interactions arepreferably mathematically modeled. The model derivatives may optionallythen be used for implementing some type of action plan. Preferably suchperson to person interactions are analyzed according to at leastimplicit communication; optionally such interactions are also analyzedaccording to direct or explicit communication. Optionally andpreferably, such a person is a consumer, in which case the modelderivatives may optionally be used to implement a marketing action plan.

According to other embodiments, a plurality of social networks isconstructed. Next a selection of at least two of the plurality of socialnetworks is combined according to a threshold of similarity, to form acombined social network.

According to some embodiments, relationships between customers aredetected by clustering a plurality of purchases according to time aswell as according to a similarity threshold for the at least one productpurchased, and then overlaying these clusters to determine at least onerelationship.

The term “up-sell” refers for example to increasing the purchase sizeduring the purchasing process by increasing the price of the productpurchased (or basket thereof), for example by inducing the consumer tobuy a more expensive product. The term “cross-sell” refers to inducingthe consumer to buy another, additional product, in addition to theproduct(s) originally requested for purchase.

As used herein, the term “product” may optionally refer to one or moregoods and/or services.

Although the present invention is described with regard to a “computer”on a “computer network”, it should be noted that optionally any devicefeaturing a data processor and/or the ability to execute one or moreinstructions may be described as a computer, including but not limitedto a PC (personal computer), a server, a minicomputer, a cellulartelephone, a smart phone, a PDA (personal data assistant), a pager. Anytwo or more of such devices in communication with each other, and/or anycomputer in communication with any other computer may optionallycomprise a “computer network”.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. The materials, methods, andexamples provided herein are illustrative only and not intended to belimiting.

Implementation of the method and system of the present inventioninvolves performing or completing certain selected tasks or stagesmanually, automatically, or a combination thereof. Moreover, accordingto actual instrumentation and equipment of preferred embodiments of themethod and system of the present invention, several selected stagescould be implemented by hardware or by software on any operating systemof any firmware or a combination thereof. For example, as hardware,selected stages of the invention could be implemented as a chip or acircuit. As software, selected stages of the invention could beimplemented as a plurality of software instructions being executed by acomputer using any suitable operating system. In any case, selectedstages of the method and system of the invention could be described asbeing performed by a data processor, such as a computing platform forexecuting a plurality of instructions.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is herein described, by way of example only, withreference to the accompanying drawings. With specific reference now tothe drawings in detail, it is stressed that the particulars shown are byway of example and for purposes of illustrative discussion of thepreferred embodiments of the present invention only, and are presentedin order to provide what is believed to be the most useful and readilyunderstood description of the principles and conceptual aspects of theinvention. In this regard, no attempt is made to show structural detailsof the invention in more detail than is necessary for a fundamentalunderstanding of the invention, the description taken with the drawingsmaking apparent to those skilled in the art how the several forms of theinvention may be embodied in practice.

In the drawings:

FIG. 1 is a schematic drawing of an exemplary, illustrative systemaccording to the present invention;

FIG. 2 is a flowchart of an exemplary, illustrative method for word ofmouth marketing according to the present invention;

FIG. 3 is a flowchart of an exemplary, illustrative method fordetermining influence of individuals having relationships according tothe present invention;

FIG. 4 is a flowchart of an exemplary, illustrative method forquantifying influence factors according to the present invention; and

FIG. 5 is a flowchart of an exemplary, illustrative method for iterativeclustering according to the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention is of a system and method for measuring or atleast detecting the effect of at least implicit communication within asocial group. Optionally also explicit communication is detected andused, but this is not necessary.

Such communication is preferably analyzed to determine influence of anindividual on one or more other individuals and/or to determine how suchan individual is influenced in turn by one or more other individuals,preferably within a social group, optionally and more preferably in thecontext of a social network. Influence may optionally be active orpassive. The passive influence is preferably measured to determine apassive transmission coefficient. More preferably, active influence isalso preferably measured to determine an active transmissioncoefficient.

With regard to implicit communication, preferably indirect methods areused to detect such influence. In other words, rather than directlyinterrogating an individual regarding communication with one or moreother individuals, preferably the behavior of the individuals isanalyzed. According to preferred embodiments of the present invention,behavior of the individuals is analyzed at least with regard to one ormore commercial transactions, for example purchasing one or moreproducts. Optionally and more preferably, commercial transactions areanalyzed to detect implicit communication, and most preferably influenceof at least one individual on at least one other individual.

Such commercial transactions may optionally be analyzed in order tofilter the data. Preferably, such analysis includes limiting potentiallyinfluencing commercial transactions to those which occur within aspecified time period. The time period is preferably determinedaccording to one or more of the following parameters: desired timeperiod for a commercial entity, length of decision cycle, expense ofproduct being purchased, durability of product being purchased and soforth. The desired time period for a commercial entity may optionally bedetermined by the entity according to any factor(s) of interest. Forexample the commercial entity may optionally only be interested inmonthly sales and changes in such sales, in which case the desired timeperiod involves transactions within a month or less of each other.

The length of the decision cycle depends upon many factors for aparticular product as is well known in the art. For example, a productthat a parent purchases for a child may have a longer decision cyclethan a similar product bought for the parent or for another adult, giventhe emotional importance of purchasing the “best” product for the childfor the parent. A summer vacation may optionally be planned on a yearlybasis, given the seasonality of such a product. Increased relativeexpense or perceived permanence of the product may also increase thedecision cycle, while lower relative expense or perceived impermanenceof the product may decrease the decision cycle.

Absolute expense is clearly an important factor, as individualstypically will increase the decision cycle for products that areabsolutely expensive for them, such as a house or automobile, which mayrequire one or more years for a decision to be reached for example.Durability is also important; a blouse which may only be worn for a fewmonths is more likely to be the subject of a short decision cycle than abread toaster for example, even if similar levels of expense areinvolved.

Another illustrative type of filter which may optionally be appliedinvolves selection for multiple (ie a plurality) of transactionsoccurring within the selected time period, representing purchases donesimultaneously by different individuals. As the number of correspondingtransactions increases, the likelihood of a relationship also increasesas does the likelihood of at least implicit communication and also ofinfluence, whether active or passive.

Yet another illustrative type of filter which may optionally be appliedinvolves selection for transactions within a geographically delimitedarea. Although influence is possible over a greater distance, passiveinfluence in particular is more likely to be increased by proximity. Thegeographically delimited area may optionally comprise one or more of aparticular region, city or town, shopping center or other building,street or other address, specific store and so forth.

Transactions tied to particular events, such as birthdays, ortransactions which are gifts, may also optionally be filtered accordingto the present invention, as such transactions are more likely to havesome type of correlation.

Of course the above description could optionally be adapted for any typeof transaction or other event or action.

According to some embodiments, the filtered data is then clustered orotherwise analyzed in order to uncover one or more relationships betweenindividuals. For example, if the relationships are displayed in a socialnetwork graph, then the individuals are the nodes and the relationshipsbetween them are indicated by edges. Non-limiting examples of suitablealgorithms for performing such clustering and/or other types of analysisinclude genetic algorithms combined with fuzzy logic, as well many knowngraphical analysis algorithms, such as dominating set, graph morphology,and so forth.

According to preferred embodiments of the present invention, the data(whether filtered or not) is analyzed according to iterative clustering.Iterative clustering is preferably used to combine availablerelationship information with commercial transactional information orother transactional information as described herein. As its namesuggest, the method is preferably performed more than once, morepreferably in order to determine at least one social network withdescribed influences in the relationships between individuals, mostpreferably including determining of a key member of the network who ismost influential.

Overall, in some embodiments, the present invention is preferably ableto align marketing strategy and budget with the actual process by whichconsumers make purchasing decisions.

Turning now to the drawings, FIG. 1 shows an exemplary system accordingto the present invention with regard to the specific example ofcommercial transactions, but also optionally including non-commercialtransactional data. As shown, a system 100 preferably features atransactional database 102 of transactional information and a personaldatabase 104 containing non-transactional information. Non-limitingexamples of transactional information preferably include informationmade regarding purchases of products. Non-limiting examples ofnon-transactional information preferably include evidence of directcommunication (for example by telephone or email); shared backgroundsuch as schools or universities attended; and any current shared lifeaspects such as shared address, near neighbors (for example on the samestreet or within a delimited geographical area), shared workplace and soforth.

Databases 102 and 104 may optionally be located at a merchant 106 asshown, such that the information contained therein is collected bymerchant 106 and may optionally be served through a database server 108.Optionally data may be available from several merchants 106 (not shown)or from open data sources (such as data from the internet) (not shown).

An analysis server 110 is preferably in communication with databases 102and 104 through database server 108. Analysis server 110 preferablycomprises an analysis module 112 for performing one or more of theanalysis methods described herein with regard to at least thetransactional data of transactional database 102, although optionallyalso with regard to the non-transactional data of non-transactionaldatabase 104. Analysis server 110 is shown as being in communicationwith a user computer 114, which optionally features a web browser 116 asa non-limiting, illustrative example the GUI (graphical user interface)for interacting with analysis module 112 and also for optionally andpreferably viewing reports, modifying, adding or removing analysisparameters and so forth. Analysis server 110 may also optionally sendreport data and/or other types of data to be stored in a database, forexample through database server 108. Analysis server 110 preferablyincludes a web server 118 for supporting communication with web browser116, for example for displaying reports and other data to the user,and/or for receiving one or more commands from the user, for example.

Database server 108, analysis server 110 and user computer 114 are alloptionally in communication through a network 120 as shown, which mayfor example optionally be the Internet. System 100 is an illustrativeexample of an implementation of a system which may optionally be usedfor performing any of the methods described herein. Even if notexplicitly described, it is assumed that any of methods described hereinmay optionally include one or more interactions with the above system100, for example for performing the method on a computer and optionallyand preferably displaying the result to the user. The term “display”optionally includes outputting data, a report or any other informationdescribed herein to at least one of a computer readable memory, acomputer display device, a computer on a network, a printer, a cellulartelephone or any other device described herein as a computer, any typeof messaging system (including but not limited to email, SMS (shortmessage service) messages or other cellular telephone messages, IM(instant messages), posting to a web site and so forth) or a user, as isknown in the art.

FIG. 2 shows a flowchart of an exemplary method for promoting word ofmouth marketing according to the present invention.

In stage 1, optionally data is analyzed to determine a social network.As described in greater detail below, the data optionally and preferablyincludes relationship data and also transactional data. More preferablythe data is analyzed according to iterative clustering. Optionally,static data (for example including but not limited to co-attendance at aschool or university or military unit, being co-workers in the past orpresent and so forth) is analyzed to form the social network. Optionallyand preferably, the static data is analyzed first, followed by othertypes of data.

In stage 2, preferably the influence of each member of the network oneach other member is determined and is more preferably measured, asdescribed in greater detail below. Such influence preferably relates tothe transactional data in order to determine how each member of thenetwork influences the purchases of one or more other members of thenetwork.

In stage 3, such influences are optionally and preferably used in one ormore marketing applications as described in greater detail below, forexample in order to direct marketing efforts particularly to key membersof the social network, who have greater influence on other members ofthe network.

FIG. 3 shows a flowchart of an exemplary method for determining aninfluence mathematical model to describe the influence a person exertsand is under from that person's social network model according to thepresent invention. For the purpose of description only and without anyintention of being limiting, the following parameters are described.

Let G=(V,E) be a directed graph describing a social network.

Where

V—A set of individuals in a society.

E—A set of directed weighted edges, each directed edge (v,u)=eεErepresents individual u who knows v or that u is influenced by v.

Let I denote the information (or opinion or perception) that spreads inthe social network. The information is optionally limited to aparticular category, for example according to a particular type ofproduct being purchased. The information preferably relates to implicitcommunication as described herein.

As shown, in stage 1, for each eεE and each I, let

denote the passive and active weights of edge e respectively with regardto information I, in which

.

The weight on the edges represents the level of influence a person v hason person u with regards to information I. Passive influence is denotedby a p superscript. Passive influence quantifies or indicates the levelin which information transferred from person A to person B withoutverbal communication simply by meeting, for example, when two peoplemeet they observe each other's clothing.

Similarly, active influence is denoted by an a superscript. Thisquantifies the level in which two people exchange information in adirect or intentional manner, for example a shows b his new webcam ortalks about investing opportunities.

Note: these weights implicitly encompass the use, opinion and regardperson v has of information I.

In stage 2, let

denote active and passive transmission coefficients for information I.These coefficients quantify the level that certain information iscommunicated (either actively or passively) between two individuals. Forexample, the passive communication factor of a shirt is higher than thatof a video camera.

In stage 3, let

denote the measure of information I within a person v.

In stage 4, the influence a person v has on person u with regards toinformation I can be described as:

In stage 5, preferably the influence on the social group by a person “v”is defined as according to the following function, which is a functionon a set:

f_(π(V))^(I) = F({f_(VU_(i))^(I)|U_(i) ∈ π(V)})

In stage 6, optionally and preferably the influence of the social groupon the person v is defined as:

f_(π⁻¹(V))^(I) = F({f_(VU_(i))^(I)|U_(i) ∈ π⁻¹(V)})

In stage 7, the results from stages 5 and 6 are optionally andpreferably used to construct one or more social variables for one ormore members of the social group. The social variables preferablyinclude the above described influences on the group and influences bythe group. Optionally the group may be divided into one or moresubgroups for such an analysis. The social variables are preferablydetermined separately for each category or type of information I. Thesocial variables also optionally and preferably include likelihood ofchurn and other decision cycle processes.

In stage 8, the results from at least stage 5 (but preferably also stage6 and optionally also stage 7) are preferably used to select a keymember of a social group, who has a greater effect on other member(s) ofthe social group than any other member. Optionally and more preferably,the key member also has more influence on the other member(s) of thegroup than the level of influence on the key member by the othermember(s) of the group.

Optionally a key member may be designated as a connector, by having atleast X members being influenced (ie a number of influenced membersabove a threshold number, which may optionally be the maximum number).The connector may then preferably be selected, for example for amarketing campaign.

Some non-limiting examples of different types of key members and theirinfluence on their social group include the following.

For example, a key member may optionally be a celebrity; under thismodel, a celebrity is a person with a very large network neighborhood,i.e. he or she is known by a great many people, and for most edges ethat represent an influence between the celebrity to another person, thevalues of

are low since the celebrity is perceived to be commercially motivated tospread information and/or commercial influence and is thereforeconsidered to be less reliable as a source of information and/orrecommendation. The effectiveness of celebrity marketing stems from thenumber of people who are influenced by him or her.

An undercover marketing person has a small network neighborhood (iesocial group), but

for his/her neighbors are higher than that of a celebrity since he/sheis perceived to be unbiased (whether true or not).

Another type of key member is an opinion leader, who is a person with anormal size network neighborhood (10-50 first-hand friends) where

his/her neighbors are very high since they are perceived to be bothunbiased and uninfluenced. The opinion leader may actually only be aleader for a particular type of information.

A key member who is an expert has a large size network neighborhoodwhere the values of

for his/her neighbors are very high since they are perceived as veryknowledgeable, but with regard to specific information I only.

Optionally, a preferred mode of influence from active or passive isselected for a marketing campaign for example according to the strengthof the relationship, more preferably with regard to a particularcategory of information. For example clothes are very visual and henceas far as fashion is concerned, one may optionally influence individualseven without a strong relationship between them. An example mayoptionally include a large social group being influenced by anindividual, even weakly; for fashion, such a situation may optionally bepreferred to a small social group with strong influence.

As a general example (without regard to a particular member type), theabove model demonstrates that viral marketing campaigns are effectiveonly when the information transition coefficients are larger than 1,such that each time a new person is “infected” with information, thisperson causes more than one person to be so “infected” as well.

Some non-limiting, illustrative examples of applications involving keymembers are as follows: surveying opinion leaders as a separate group tomake value offering more in tune with their needs (and hence toinfluence their followers); targeting opinion leaders as a way to reachthe entire market; timing and/or otherwise arranging marketing campaignsto increase existing word of mouth (for example, first target opinionleaders, then after a time their followers, then the followers of thesefollowers and so forth).

Another exemplary, non-limiting, illustrative example of an applicationwith a key member and one or more influenced members is to detect and/ordetermine when the key member has implicitly communicated information tothe one or more influenced members, and then to contact the latter tofurther influence their selection and/or actions, for example toinfluence them to make a purchase. Optionally and preferably, themarketing campaign may be constructed to first directly attempt toinfluence a key member, for example with a coupon or special deal, suchthat the probability of communication increases between the key memberand the one or more influenced members.

If a plurality of key members is available for such a campaign, thenoptionally and preferably one or more key members who are part of thesame social network and/or who share more of the same overlapping socialgroup are selected in order to maximize influence on a selected group ofinfluenced members.

Also optionally a social network may be divided into a plurality ofsmaller sub-networks (micro social segments), after which a marketingcampaign is then directed to one or more selected sub-networks, forexample in order to achieve a desired level of saturation in that partof the market as a whole and so forth. Also, this method permits thedevelopment and management of a marketing campaign from a socialdevelopment perspective, for example by selecting certain sub-networksto initiate a campaign and then propagating the campaign throughout thenetwork.

FIG. 4 is of an exemplary, illustrative method for applying the modelconstructed according to FIG. 3, for example, for each member v andinformation I, measuring

or in other words, try to measure how much a member is influencing andis influenced by his social environment with regards to certaininformation or opinion I. Overall, this method involves analyzing thetransactions a member performs with the organization to detect suchinfluences. Since each individual's decisions are dependent oninteractions with one or more other people, the interdependency betweenindividuals is reflected in the data. The below method may optionally beused to uncover this interdependency and to measure

with a high degree of accuracy, preferably with regard to implicitcommunication as described herein. Optionally and preferably, the memberof the social group and/or network is a customer or potential customer.

Turning now to the drawing, as shown in FIG. 4, in stage 1, let

denote the active or passive possible distinct event of v influencing uwith measured energy e. in most cases while influence is continuous,measurable influence

as e is measured when u interacts with the organization and that is ayes/no event. The influence is determined to be active or passive basedon the type of information (tangible/non tangible), is the informationnot portable and not accessible to u (for example, a vacation is notportable, as it cannot be brought or shown to others later; it is alsonot accessible unless u participates in the vacation)

And similarly to p.

As for FIG. 3, let I denote the information, inclination or content tobe transferred within the social network. Let

denote an edge in a graph describing a social network.

Let

denote the correlation function between all possible I's and a specificI. So for example f describes the average influence of an individualbased on combined influences for many different types of informationetc. The value of f is calculated from the data in stage 2.

In stage 3, the following equation is obtained:

This equation permits quantification of

which are the influence factors in stage 4.

Optionally one or more heuristics may be applied to filter or conditionthe above analyzed data, in order to identify influence. For example,once a social connection has been detected (optionally by filtering aspreviously described), a filter may be applied to measure theprobability that A influences B by taking statistically distinct contenttransactions. Another filter is determining transactions with a similarcontent and with a temporal sequence, indicating a greater possibilityof influence. Detecting transactions with a similar content mayoptionally be performed by overlaying other database transactionsavailable from open sources or other verticals or from one or moredatabases such as identified Internet transactions.

As a non-limiting example of an application of the above method,consider the following. Let G be a directed graph describing the socialnetwork of the buyers at a particular retail chain. Let I₁ denoteshopping at the store.

I₂ denote purchasing a particular SKU or SKU group.

denotes and event by which person v can influence person u. For example,person v buys a blouse and if person u buys the same blouse within agiven time period, such as for example 3 weeks, than e=1, otherwise e=0.Since the blouse is shown (ie is visible to the person potentially beinginfluenced), preferably passive influence is first quantified.

In this example, person v had 60 opportunities to influence u to buy acertain model, out of which person v did effect such influence 5 times.The influence weight is

where C is the constant derived from the dataset.

Similarly, assume that the

By looking at all people in the dataset, there is a correlation betweenpeople that have an influence in the model, and actually inducing aninfluenced person to shop at the store.

FIG. 5 is a flowchart of an exemplary, illustrative method for iterativeclustering according to the present invention. As shown in stage 1, dataregarding potential direct relationship information is obtained.Non-limiting examples include evidence of direct communication (forexample by telephone or email); shared background such as schools oruniversities attended; and any current shared life aspects such asshared address, near neighbors (for example on the same street or withina delimited geographical area), shared workplace, children or spouseswhich have share educational institute (school) or workplace and soforth.

In stage 2, this data is analyzed, preferably first by weighting theinformation according to likelihood of a shared relationship, and alsooptionally by combining relationship data from several sources in orderto strengthen the possibility of a relationship. The type of analysispreferably comprises one or more clustering type algorithms, includingbut not limited to, K-means algorithm, Fuzzy C-means, QT clustering,agglomerative hierarchical clustering and so forth.

Optionally and preferably stages 1 and 2 are repeated for differenttypes of direct relationship information, for example according tocomplexity thereof. For example, optionally a geographical area isexpanded in order to capture additional relationship information,thereby increasing the complexity, such that stages 1 and 2 are firstpreferably performed for a more limited geographical area, andthereafter repeated (ie more preferably performed iteratively) for moreexpansive geographical area(s).

In stage 3, optionally the potential relationships are sorted accordingto likelihood, whether with regard to a cut-off threshold, relativeranking or any other method.

In stage 4, transaction data, which is optionally and preferablycommercial transaction data, is analyzed according to the previouslydetermined relationships. If the relationships are sorted, then the mostlikely relationships are considered first with regard to the (optionallycommercial) transaction data in order to determine an influential(optionally-commercial) transaction relationship between two or moreindividuals.

Stage 4 is optionally and preferably performed repeatedly, again morepreferably according to increasing complexity of the transaction databeing analyzed, such that the analysis is more preferably performedidentified.

Furthermore, the analysis produced from stages 1 and 2 is optionally andpreferably used to reduce the complexity of the transactional data forthe analysis in stage 4, particularly for large data sets (for exampletransactional data or purchases for a major credit card company).

In stage 5, optionally a group of such individuals is determined.Preferably in stage 6, a key member of the group is identified, who aspreviously described is more influential than the others on commercialtransactions.

In stage 7, optionally and preferably a social network of suchindividuals is determined (this stage may optionally be performed afterstage 5 or in place of stage 5, and may also optionally be performedbetween stages 3 and 4), followed by determining the influence betweensuch individuals in stage 8 (optionally, if stage 7 is performed betweenstages 3 and 4, stage 8 may optionally be combined with stage 4).

Marketing Uses of Model Derivatives

This example relates to various illustrative, non-limiting methods foremploying the information obtained as described previously, particularlywith regard to FIGS. 3 and 4.

Prediction

These quantities can now be used in any econometric predictive model asinputs. This allows any existing statistical model to be far moreaccurate.

For example: Let I denote the position on resigning a cellular service.For each customer v input the then current

preferably input into a statistical model in an attempt to predict chum.In other words, in predicting chum, not only the transactional historyof the individual is considered, but also actual chum and chumpossibilities by the individual's social network members (for examplefriends and family). Thus, the influences throughout a social networkmay optionally comprise a “social variable” which is then preferablyinput to a statistical model in conjunction with other variables.

Measure>Design>Implement>Measure

These quantities can also be used to measure how they are affected bythe organization's marketing efforts. For example, if a certain producthas

at time t₀ and the organization wished to increase the word of mouthgenerated, the organization can measure

at time t₀ using the methods described herein, design a differentmarketing approach, implement the new approach and then measure thechange in

time t₁.

Increasing Trends

The above methods may also optionally be used to identify trends andincrease or preempt them, optionally and preferably by repeatedlymeasuring

over time for every v.

EXAMPLE 1 For Implementation—Retailer

This technology has been implemented successfully in a retailer. Thefollowing is a short description of the process:

Retailer Description

A clothing retailer consisted of 100,000 regular customer has beenanalyzed. Customers make purchases 1-2 times a year on average in thisretailer.

Analysis Process

For the purposes of increasing marketing goals, the retailer has definedseveral information types:

I₁—The decision to start purchasing at the retailer.

I₂—The decision to cease purchasing at the retailer.

I₃ 13 Selecting a specific fashion model or clothing line.

I₄—Make a purchase.

The database provided by the merchant was then analyzed by applyingalgorithms to calibrate the social influence

with respect to each information type. The social influence(s) for eachof the 100,000 customers in the database was then determined.

Uses:

1. Churn Prediction—

The purpose of this activity is to rank the customers that are mostlikely to resign from purchasing at the retailer within the next 6months from Nov. 1, 2007. The retailer preformed this analysis usingclassic data mining statistical algorithms, in this case decision trees,clustering and logistical regression. A training set was defined as allcustomers who were frequent customers on Nov. 1, 2006. The algorithmused the training set variables to try and predict the results of whichcustomer has churned. By the time the algorithm was run, the resultshave been already known. The input variables contained about 40different variables including all standard parameters for this analysissuch as frequency, monetary, as well as model purchasing, favorite shopin the chain, demographical attributes etc. Moreover changes in behavior(as measured by any change in variables over time) were also input.

These algorithms select variables that are independent of each other(for example, winter purchases were correlated with winter modelselection so one of them is enough). Later the algorithms rank variablesby their ability to predict accurately.

The most predictive variable was the total influence on a person to makea purchase, or

The more a person is influenced to make a purchase by his socialenvironment, the less likely he is to stop shopping. The second mostpredictive variable was the money a person spent with the retailer. Theretailer launched a campaign on November 15^(th) to prevent churn ofcustomer with highest risk of chum.

2. Churned Opinion Leader Identification—

The purpose of this activity is to find customers that have stoppedshopping and have caused their social environment to stop shopping. Forthis purpose,

was calculated for each v. The values were ranked. A survey of several vconfirmed that they indeed caused their friends to stop shopping, insome cases even actively boycotting the retailer (for example due toperceived poor treatment during a sale and/or attempted return of aproduct or other customer service experience). As a result the CEO ofthe retailer decided to change several policies, including the itemreturn policy.

3. Win back. The purpose of this is to find customers that have stoppedshopping and increase the social influence on them to start shoppingagain

There is a second degree of optimization in this case as individualswith the greatest influence on others who have churned are preferablyselected. As a result, the retailer has launched a campaign that targetsopinion leaders with large churned neighborhoods (ie social networks ofmany individuals who themselves churned).

EXAMPLE 2 For Implementation—Hotel

As described above, according to some embodiments of the presentinvention, there is provided a method for constructing a marketingapplication, such as a marketing campaign, by building a social network;determining influence of one or more members on each other through thenetwork; and then constructing the marketing campaign (for example)according to such influences, for example by marketing preferentiallyand/or differently to a key member of the network.

For this example, the above method is optionally and preferablyimplemented as follows. For building the network, the followingillustrative situation is considered. A hotel chain has transactionalinformation in the database related to booking and check in data. Tobuild the social network, this data is analyzed to determine thelikelihood that two individuals, for example, are connected.Non-limiting examples of transactional data parameters which mayoptionally support such a connection include staying in the same hotelfor the same time, having similar check in identifiers, making otherpurchases and activities together, and/or similar or parallel changes intheir respective reservations. These parameters are preferably analyzedto determine statistical distinctiveness.

Once the network has been constructed, preferably the influence of oneor more members on each other is determined according to relationshipsin the social network. Influence is preferably determined according toimplicit communication occurring with statistical distinctiveness,including but not limited to detecting or measuring events that occurredwith statistical distinctiveness by time or type, as well as determiningindividuals who actually make the booking, who make the first purchases,who travel more, who spend more nights at the hotel chain and so forth.

Once these influences have been identified, preferably one or more keymembers of the group (ie more influential members of the group) aretargeted in a marketing application or campaign, for example to increasetheir loyalty through various known techniques and to increase thelikelihood of a successful word of mouth campaign through theirinfluence on other member(s) of the group.

Such an implementation is a non-limiting example for a purchase that mayoptionally have a long decision cycle or a short decision cycle. Theshort decision cycle may be apparent for example for business travelers,who may need to travel at short notice and so may make rapid decisionson hotels. The long decision cycle may be apparent for example fortourists traveling for a summer vacation, who may make their decisionover several months or even a year. Thus, depending upon the type oftravel performed by the members of the social network, the period overwhich influence is considered is preferably adjusted to incorporate along or short decision cycle, or both.

EXAMPLE 3 For Implementation—Telecommunication

Another example is provided herein with regard to implementation of themethod according to the present invention for marketing for atelecommunication provider, such as a service provider for example.

To build the social network, optionally and preferably transactionaldata from the service provider's database is used, for example acellular telephone service provider. The cellular provider's databasecontains transactions that describe explicit communication, such astelephone calls, SMS or other messages and other types of communication.Optionally explicit communication is used at least partially toconstruct the social network. However, more preferably other types ofinformation are used as well, including but not limited to static dataas described herein, including but not limited to address, age,co-attendance at a particular school, university, army unit and thelike; type of purchasing contract and so forth. Other types of implicitinformation includes individuals communicating within the same cellularcell on a repeated, preferably frequent basis; or communicating withinthe same isolated cellular cell (having fewer users), for example;and/or other geographical or other implicit information. Implicit datais preferably selected for use in building the social network afterdetermining that is statistically distinct, for example according to anysuitable statistical measure.

Once the social network has been constructed, further processing ispreferably performed to determine relevance of the relationship.Alternatively, such processing may be performed before or duringconstruction of the social network. An average person will communicatewith 150 different individuals a year. The time spent with each person,the time of call and other communication parameters are not sufficientto describe the relationship between individuals. For example, anindividual may communicate with a trusted friend once a week for severalminutes to set up a meeting, while communicating several times per weekover an extended period with a plumber.

To qualify the relationship as meaningful, preferably a plurality ofdifferent types of implicit communication is combined with the explicitcommunication data. Non-limiting examples of such implicit data includedetecting individuals who communicate from and/or live and/or work inthe same geographical location at the same time, and/or who purchase thesame or similar products at the same or similar time, and so forth, toform implicit parameters through clustering of data. The cellularprovider also has transactions describing purchases made with thecellular provider such as device or service purchase, which forms datato be analyzed to detect and/or measure implicit communication.

This implementation was tested with transactional data from the databaseof a cellular telephone service provider. When introducing theseimplicit parameters more than 90% of the network definition changeddrastically.

Next, influence is preferably determined. The constructed social networkserved as a basis to determine influence. Influence on events (such asdevice upgrade) that occurred with statistical distinctiveness by timeor model type for example was measured as previously described.

The method also enabled measurement of several social phenomena such aschurn. It was determined that friends of individuals (subscribers) whochurned were influenced such that they were ten times more likely tochurn themselves. However, these individuals were only two times morelikely to churn due to influence from churning subscribers with whomthey had a great deal of communication but by whom they were notinfluenced.

The method also enabled detection of the opinion leaders (key members)of the social network who in fact drove churn in the social network;typically each caused 3-5 other subscribers to churn.

Next, optionally and preferably a marketing campaign is constructedaccording to the above information. For this example, the subscriberswho are at high risk of churning due to social influence are preferablytargeted through one or more marketing efforts to prevent them fromchurning.

The above method also was used to identify one or more opinion leaderswho may optionally also be specifically targeted, for example optionallythrough application of one or more tools that are intended to make themmore involved with the organization and to retain them as loyalcustomers.

EXAMPLE 4 For Implementation—Internet Data Analysis

The Internet includes a great deal of explicit communication, such asemail messages that are sent from one individual to one or morerecipients. Furthermore, the Internet also features explicit socialnetworks. However, this explicit information is frequently much lessvaluable and informative than implicit information. The latter is moredifficult to detect and analyze, and so has not been considered inprevious attempts to mine Internet data.

An example of such implicit information occurs when a plurality ofindividuals post a response to a blog or other Internet forum or site,not directly to each other, but rather on the same subject or post, orat similar times or with similar content, or displaying the sameinterests, or a combination of the above. Another example occurs for aplurality of users that share a common interest, for example sports suchas skiing, running, bicycling, scuba diving and so forth, and who visitthe same website. This information is preferably not used to segment thepopulation of such visitors according to interests, but rather to detectthe presence of an actual relationship within such data.

The potential connections detected according to such an analysis arepreferably incorporated into the previously described iterativeclustering method. For example, for visitors to one or more scuba divingsites, optionally and preferably their potential connections are furtheranalyzed according to commercial transactional data (such as purchasedata and so forth) as previously described, to detect one or more actualrelationships as previously described. The data regarding visitors toone or more scuba diving sites is optionally and preferably used toreduce the complexity of the transactional data, particularly for largedata sets (for example transactional data or purchases for a majorcredit card company). The Internet is a non-limiting example for anytype of open source data or commercially purchasable large datacollections.

According to other embodiments, most if not all information isdetermined according to actions, activities and interactions through theInternet. For example, click analysis of visitors to a web site isperformed, optionally to determine whether their computer have the sameIP address for example, and/or whether a plurality of visitors all enterdirectly to a web page within the web site (apart from the landing orhome page, such as a specific article for example), and not through asearch engine, which may optionally indicate a referral. The content ofsuch web sites may also optionally be analyzed, for example to determinea common interest (such as scuba diving), a common geographical area andso forth. The common geographical area may optionally be expanded forInternet based analyses.

Further click analyses may optionally be performed to attempt todetermine an implicit connection. For example, a first visitor to anarticle deep within a web site may optionally reach such an articlethrough a search engine, while a second visitor directly enters to thearticle without a search engine and without viewing any other page ofthe web site. The second visitor may therefore optionally have beenreferred by the first visitor, thereby indicating a potentialconnection.

EXAMPLE 5 For Implementation—Other Types of Transactions

The above examples related to commercial transactions, particularly atretail stores. However, the present invention may optionally be appliedto a variety of different commercial transactions, including but notlimited to one or more of telecoms (including cellular, local telephoneservice providers and long distance providers); credit card providers;credit unions; retail banking; retail financial services (including butnot limited to one or more of insurance and investments); restaurants;travel agencies and/or purveyors; airlines; train, bus and othertransportation providers; hotel and resort chains and/or singleestablishments; health services, country clubs, sports centers, spa andhealth centers; and supermarkets, pharmacies and other purveyors ofconsumable goods. A table is provided below, indicating somenon-limiting, illustrative examples of influence factors and/ortransactional factors which may be analyzed according to the presentinvention.

Transactions Location Content Gifts Telecoms Calls with The Purchase ofspecific correlation product or frequency, between service, duration,and originating interaction reciprocity, cells of two people withinternet originating content, Cell, specific interaction time of day,with specific week or year. business vertical phone numbers Credit CardProviders Purchases Purchases Purchase at Credit Unions made with madewith similar or close time proximity same retailers proximitycorrelation with similar (minutes) in outside the 3 purchase the samestore sigma normal price. repeatedly location distribution within aclose proximity of time (days). Retail Banking ATM Withdrawals Purchaseor Money withdrawals. made with registration transfers Money proximityof financial between transfer in or correlation service or individualsout from a outside the 3 product from employee sigma normal the bank.business or location individual. distribution within a close proximityof time (days). Various types Store Visit, Purchases SKU purchase. ofretailers purchased made with SKU. proximity correlation outside the 3sigma normal location distribution within a close proximity of time(days). Travel Mutual Same Selection of Agencies, booking, destinationspecific Airlines, Similar and travel travel times Train, bus andseating, dates. and places. other consecutive transportation or closecheck providers. in time, Hotel and Resort chains or individualestablishments. Health Selection of Selection of Services Doctors,specific Filling service prescriptions provides, doctors or medicines.

Other non-limiting examples of applications of the present inventioninclude: creating trends; micro segmentation of markets; focusingmarketing activities in specific communities (saturating communities,for instance); adding all of the influence metrics as input tostatistical modeling; fraud detection; contagious disease spreadinganalysis and prevention; and crime activity monitoring and prevention.

It should be noted that optionally any method for building a socialnetwork as described above may be employed, alone or in combination, toactually construct the social network itself, after which the networkmay optionally be used in a different application than that describedabove. For example, a social network constructed for marketing mayoptionally be used for infectious disease prediction, tracking,prevention and control, and/or crime activity monitoring and prevention,and so forth.

Also optionally a social network built for another purpose, for examplean Internet or web-based social network such as Facebook for example,may be used for one or more of the above described methods ofdetermining influence, identifying key members of a group or socialnetwork, and so forth.

While the invention has been described with respect to a limited numberof embodiments, it will be appreciated that many variations,modifications and other applications of the invention may be made.

1. A method for determining a relationship of a first individual to a second individual, comprising: Detecting implicit communication according to at least one transactional event; and Determining the relationship according to influence through said implicit communication.
 2. The method of claim 1, wherein said transactional event relates to a commercial transaction.
 3. The method of claim 2, wherein said commercial transaction relates to a purchase of a product.
 4. The method of claim 2, wherein said commercial transaction relates to purchase of a subscription.
 5. The method of claim 4, wherein said commercial transaction relates to one or more of churn, client acquisition, up-sell, cross-sell or win-back, or a combination thereof.
 6. The method of claim 5, wherein said commercial transaction relates to a company and wherein said commercial transaction further comprises contacting said company or changing at least one parameter of said commercial transaction.
 7. The method of claim 6, further comprising: Determining at least one marketing action according to the relationship.
 8. The method of claim 7, wherein said at least one marketing action comprises one or more of increasing brand awareness, churn prevention, client retention, client acquisition, up-sell, cross-sell or win-back, or a combination thereof.
 9. The method of claim 8, further comprising detecting explicit communication and also determining the relationship according to said explicit communication.
 10. The method of claim 9, wherein said detecting said explicit or implicit communication further comprises: Determining an active transmission coefficient for active transfer of information; and/or Determining a passive transmission coefficient for passive transfer of information.
 11. The method of claim 10, wherein said transaction has a long decision cycle.
 12. The method of claim 10, wherein said transaction has a short decision cycle.
 13. The method of claim 12, further comprising: Determining a plurality of relationships; and Constructing a social group from said plurality of relationships.
 14. The method of claim 13, further comprising: Constructing a social network from said social group.
 15. The method of claim 14, further comprising: Determining a plurality of relationships; and Constructing a social network from said plurality of relationships.
 16. The method of claim 15, further comprising: Constructing a plurality of social networks; and Overlaying said plurality of social networks to combine into a combined social network according to a similarity threshold.
 17. The method of claim 16, further comprising: Determining an influence in said relationship.
 18. The method of claim 17, wherein said influence is active and/or passive.
 19. The method of claim 18, further comprising: Determining a strength of said relationship; and Selecting a preferred mode of influence from active or passive according to said strength of said relationship.
 20. The method of claim 19, wherein said implicit communication is performed through the Internet.
 21. The method of claim 20, wherein said implicit communication through the Internet includes one or more of e-mail, posting on a web page, posting on a blog, posting on a chat room, visiting a web page or an IM (instant messenger) message.
 22. The method of claim 21, further comprising analyzing said implicit communication according to content to detect a potential relationship.
 23. A method for constructing a social group of a plurality of members, comprising: Detecting at least one relationship between the plurality of members; Determining active or passive coefficients for active or passive transfer of information for at least implicit communication between each plurality of members having said relationship; Analyzing said implicit communication according to transfer of information to determine influence; and Detecting at least one key member of the social group according to said influence.
 24. The method of claim 23, wherein said social group comprises a social network.
 25. The method of claim 24, wherein said implicit communication relates to one or more transactions.
 26. The method of claim 25, wherein said one or more transactions include at least one commercial transaction.
 27. The method of claim 26, wherein said at least one commercial transaction relates to purchase of a product.
 28. The method of claim 26, wherein said at least one commercial transaction relates to purchase of a subscription.
 29. The method of claim 28, wherein said at least one commercial transaction relates to one or more of churn, client acquisition, up-sell, cross-sell or win-back, or a combination thereof.
 30. The method of claim 29, wherein said commercial transaction relates to a company and wherein said commercial transaction further comprises contacting said company or changing at least one parameter of said commercial transaction.
 31. The method of claim 30, further comprising: Determining at least one marketing action according to the relationship with said key member.
 32. The method of claim 31, wherein said at least one marketing action comprises one or more of increasing brand awareness, churn prevention, client retention, client acquisition, up-sell, cross-sell or win-back, or a combination thereof.
 33. The method of claim 32, further comprising: Detecting a plurality of key members; Determining a number of members influenced by each key member; and Selecting a particular key member as having at least a threshold number of influenced members.
 34. The method of claim 33, wherein said threshold comprising having the largest number of influenced members.
 35. The method of claim 34, further comprising: Addressing at least one marketing method to said particular key member.
 36. A method for revealing a social network among a plurality of individuals, comprising: detecting implicit communication between said plurality of individuals; determining one or more relationships at least according to said implicit communication; and constructing the social network according to said one or more relationships.
 37. The method of claim 36, further comprising: Constructing a plurality of social networks; and overlaying said plurality of social networks to form a combined network.
 38. A method for detecting a purchasing relationship between a plurality of customers, comprising: Constructing a plurality of social networks, wherein at least one network is related to purchases in time and wherein at least one network is related to purchases of similar goods; Overlaying said plurality of social networks; and Combining at least two of said plurality of social networks having at least a threshold degree of similarity.
 39. The method of claim 38, wherein at least one social network is constructed according to implicit communication.
 40. The method of claim 39, wherein at least one social network is constructed according to explicit communication.
 41. A method for detecting a purchasing relationship between a plurality of customers, comprising: Providing purchasing data related to purchases according to time and according to similarity of goods; Clustering said purchasing data related to purchases according to time and/or space and according to simlarity of goods; Overlaying said clusters; and Selecting at least one cluster pair in time and similarity having at least a threshold likelihood value.
 42. A method for determining information flow through a social network, comprising: Determining a relationship between at least two members or the social network; Analyzing transactional data according to said relationship; and Determining flow of information according to said analyzed transactional data.
 43. The method of claim 42, wherein at least one social network is constructed according to implicit communication.
 44. The method of claim 43, wherein at least one social network is constructed according to explicit communication.
 45. The method of claim 44, further comprising determining one or more characteristics of at least one member of the social network according to information flow.
 46. The method of claim 45, further comprising: Determining an influence of at least one member in said relationship; and Determining information flow also according to said influence.
 47. A method for constructing at least one social variable for al individual who is part of a social group of a plurality of individuals, comprising: Determining influence of the individual on the plurality or individuals for at least one topic; Determining influence of the plurality of individuals on the individual for said at least one topic; and Constructing the at least one social variable according to said influence or the individual on the plurality of individuals and said influence of the plurality of individuals on the individual for said at least one topic.
 48. The method of claim 47, wherein said influence is determined for a plurality of commercial topics.
 49. The method of claim 48, wherein said commercial topics include at least churn potential.
 50. The method of claim 49, wherein said determining said influence comprises detecting implicit communication according to at least one transactional event; and Determining said influence through said implicit communication.
 51. The method of claim 50, further comprising dividing said social group into a plurality of sub-groups before determining said influence. 